Criteria for training fuzzy classifier taking into account the cost matrix
Abstract
The tie "input – output" is described by linguistic if – then rules where antecedents contain fuzzy terms "low", "medium", "high" in the fuzzy classifiers. To enhance the correctness it is necessary to train fuzzy classifier on experimental data. The paper extends the criteria for training fuzzy classifier to the case of the cost matrix, which consist of costs of the different types of errors. Computer experiments on the task of heart disease diagnosis have shown that the best quality setting enables the use of the training criterion, in which the distance between the fuzzy inference results and experimental data for the cases of misclassification is multiplied by penalty coefficient.References
1. Штовба С. Д. Порівняння критеріїв навчання нечіткого класифікатора / С. Д. Штовба // Вісник Вінницького полі-
технічного інституту. — 2007. — № 6. — С. 84—91.
2. Scott C. Neyman-Pearson approach to statistical learning / Scott C., Nowak R. A // IEEE Transactions on Information
Theory. — 2005. — Vol. 51, № 11. — P. 3806—3819.
3. Штовба С. Д. Проектирование нечетких систем средствами MATLAB / С. Д. Штовба. — М. : Горячая линия —
Телеком, 2007. — 288 с.
4. Ishibuchi H. Voting in fuzzy rule-based systems for pattern classification problems/ Ishibuchi H., Nakashima T., Morisawa
T. // Fuzzy Sets and Systems. — 1999. — Vol. 103, № 2. — P. 223—238.
5. Rotshtein A. Design and tuning of fuzzy rule–based system for medical diagnosis. In «Fuzzy and Neuro-Fuzzy Systems in
Medicine» (Eds. : Teodorescu N. H., Kandel A., and Jain L. C.) / A. Rotshtein. Boca-Raton : CRC-Press, 1998. — P. 243—289.
6. Shtovba S. Tuning the fuzzy classification models with various learning criteria: the case of credit data classification /
Shtovba S., Pankevich O., Dounias G. // Proc. of Inter. Conference on Fuzzy Sets and Soft Computing in Economics and
Finance. St. Petersburg (Russia), 17—20 June 2004. — Vol. 1. — St. Petersburg : Russian Fuzzy Systems Association, 2004. —
P. 103—110.
7. Растригин Л. А. Адаптация сложных систем. Методы и приложения / Л. А. Растригин. — Рига : Зинатне, 1981. —
375 с.
8. Bache K., Lichman M. UCI Machine Learning Repository [Электронный ресурс] / Bache K., Lichman M. Irvine, CA: University
of California, School of Information and Computer Science. — 2013. — Режим доступу : http://archive.ics.uci.edu/ml.
9. Штовба С. Д. Обеспечение точности и прозрачности нечеткой модели Мамдани при обучении по эксперименталь-
ным данным / С. Д. Штовба // Проблемы управления и информатики. — 2007. — № 4. — С. 102—114.
технічного інституту. — 2007. — № 6. — С. 84—91.
2. Scott C. Neyman-Pearson approach to statistical learning / Scott C., Nowak R. A // IEEE Transactions on Information
Theory. — 2005. — Vol. 51, № 11. — P. 3806—3819.
3. Штовба С. Д. Проектирование нечетких систем средствами MATLAB / С. Д. Штовба. — М. : Горячая линия —
Телеком, 2007. — 288 с.
4. Ishibuchi H. Voting in fuzzy rule-based systems for pattern classification problems/ Ishibuchi H., Nakashima T., Morisawa
T. // Fuzzy Sets and Systems. — 1999. — Vol. 103, № 2. — P. 223—238.
5. Rotshtein A. Design and tuning of fuzzy rule–based system for medical diagnosis. In «Fuzzy and Neuro-Fuzzy Systems in
Medicine» (Eds. : Teodorescu N. H., Kandel A., and Jain L. C.) / A. Rotshtein. Boca-Raton : CRC-Press, 1998. — P. 243—289.
6. Shtovba S. Tuning the fuzzy classification models with various learning criteria: the case of credit data classification /
Shtovba S., Pankevich O., Dounias G. // Proc. of Inter. Conference on Fuzzy Sets and Soft Computing in Economics and
Finance. St. Petersburg (Russia), 17—20 June 2004. — Vol. 1. — St. Petersburg : Russian Fuzzy Systems Association, 2004. —
P. 103—110.
7. Растригин Л. А. Адаптация сложных систем. Методы и приложения / Л. А. Растригин. — Рига : Зинатне, 1981. —
375 с.
8. Bache K., Lichman M. UCI Machine Learning Repository [Электронный ресурс] / Bache K., Lichman M. Irvine, CA: University
of California, School of Information and Computer Science. — 2013. — Режим доступу : http://archive.ics.uci.edu/ml.
9. Штовба С. Д. Обеспечение точности и прозрачности нечеткой модели Мамдани при обучении по эксперименталь-
ным данным / С. Д. Штовба // Проблемы управления и информатики. — 2007. — № 4. — С. 102—114.
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How to Cite
[1]
S. D. D. Shtovba, O. D. Pankevych, and A. V. Nahorna, “Criteria for training fuzzy classifier taking into account the cost matrix”, Вісник ВПІ, no. 6, pp. 84–90, Dec. 2013.
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